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rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning

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  • Kursa, Miron B.

Abstract

Random ferns is a very simple yet powerful classification method originally introduced for specific computer vision tasks. In this paper, I show that this algorithm may be considered as a constrained decision tree ensemble and use this interpretation to introduce a series of modifications which enable the use of random ferns in general machine learning problems. Moreover, I extend the method with an internal error approximation and an attribute importance measure based on corresponding features of the random forest algorithm. I also present the R package rFerns containing an efficient implementation of this modified version of random ferns.

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  • Kursa, Miron B., 2014. "rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i10).
    • Handle: RePEc:jss:jstsof:v:061:i10
    DOI: http://hdl.handle.net/10.18637/jss.v061.i10
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    Cited by:

    1. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.

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